APPLICATION OF CLUSTERING METHODS IN RECOMMENDER SYSTEMS FOR USER BEHAVIOR ANALYSIS (ENVIRONMENT. TECHNOLOGY. RESOURCES. Proceedings of the International Scientific and Practical Conference, 2025)
Recommender systems are crucial in personalizing digital experiences by predicting user preferences. This paper examines the application of the k-means clustering algorithm in recommender systems to segment users based on behavioural patterns, enhancing recommendation accuracy and efficiency. The study also compares k-means with other clustering techniques, analyzing their advantages and limitations in handling sparsity and the cold start problem. The results highlight the effectiveness of k-means for improving user segmentation and optimizing recommendation strategies.